Oral Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

Proteogenomics — connecting cancer genotype with molecular phenotype (#128)

Janne Lehtio 1
  1. Science for Life Laboratory, Dep of Oncology and Pathology, KAROLINSKA INSTITUTET, SOLNA, SE

The explosion of genomics data has improved our understanding of cancer greatly in recent years. However, the knowledge of how genomic aberrations affect the functional proteome at the systems level is still very limited. Proteome data represents the combined effect of epigenetic, transcriptional and translational regulation and will therefore provide an important molecular phenotype data layer for multi-omics analysis. To allow effective systems biology analysis including proteomics, we have generated tools that take advantage of massive genomics data by incorporating sequence information to the proteomics data-analysis pipeline. This will allow protein level analysis of gene variants as well as detection of novel protein coding regions1. To control error rate in variant detection, we have a combined experimental isoelectric point data from peptide fractions (HiRIEF LC-MS/MS) and bioinformatics approaches into the proteogenomics workflow (IPAW)2. A proteogenomics analysis of histologically human tissues using the IPAW pipeline reveals novel coding regions. When applied on breast cancer tumor sample, we could demonstrate in-depth quantitative analysis revealing drug target interesting correlations as well as discovers putative cancer neoantigens3. To gain knowledge of the novel proteins, we analyzed the subcellular location of these in human cell line models. For location analysis, we used SubCellBarcode based proteome wide location analysis4. Further, the single amino acid variant detection pipeline enabled detection of paternal and maternal proteins transferring placenta during pregnancy, suggesting molecular communication between fetus and mother5.


  1. Branca M., Orre L-M., Johansson H.J., Granholm V., Huss M., Pérez-Bercoff Å., Forshed J., Käll L., Lehtiö J. HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nature Methods, 2014 Jan;11(1):59-62.
  2. Zhu Y, Orre LM, Johansson HJ, Huss M, Boekel J, Vesterlund M, Fernandez-Woodbridge A, Branca RMM, Lehtiö J. Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow. Nature Commun. 2018 Mar 2;9(1):903.
  3. Johansson HJ, Socciarelli F, Vacanti NM, Haugen MH, Zhu Y, Siavelis I, Fernandez A, Aure MR, Sennblad B, Vesterlund M, Branca RM, Orre LM, Huss M, Fredlund E, Beraki E, Garred Ø, Boekel J, Sauer T, Zhao W, Nord S, Höglander EK, Jans CD, Brismar H, Haukaas TH, Bathen TF, Schlichting E, Naume B, OSBREAC, Luders T, Borgen E, Kristensen VN, Russnes HG, Lingjærde OC, Mills GB, Sahlberg KK, Børresen-Dale L, Lehtiö J., Breast cancer quantitative proteome and proteogenomic landscape. Nature Comm, 10 Apr, 2019.
  4. Orre LM, Vesterlund M, Pan Y, Arslan T, Zhu Y, Fernandez Woodbridge A, Frings O, Fredlund E, Lehtiö J. SubCellBarCode: Proteome-wide mapping of protein localization and relocalization. Mol Cell. 2019 Jan 3.
  5. Pernemalm M, Sandberg A, Zhu Y, Boekel J, Tamburro D, Schwenk JM, Bjork A, Wahren-Herlenius M, Amark H, Ostenson C.G, Westgren M, Lehtiö J. In-depth human plasma proteome analysis captures tissue proteins and transfer of protein variants across the placenta. eLife, Apr. 8., 2019.